KLighD VS LAGraph

>Graph drawing tools

It's hard

Graphviz-like generic graph-drawing library. More options, more control.

https://eclipse.dev/elk/

Experiments by the same team responsible for the development of ELK, at Kiel University

https://github.com/kieler/KLighD

Kieler project wiki

https://rtsys.informatik.uni-kiel.de/confluence/display/KIEL...

Constraint-based graph drawing libraries

https://www.adaptagrams.org/

JS implementation

https://ialab.it.monash.edu/webcola/

Some cool stuff:

HOLA: Human-like Orthogonal Network Layout

https://ialab.it.monash.edu/~dwyer/papers/hola2015.pdf

Confluent Graphs demos: makes edges more readable.

https://www.aviz.fr/~bbach/confluentgraphs/

Stress-Minimizing Orthogonal Layout of Data Flow Diagrams with Ports

https://arxiv.org/pdf/1408.4626.pdf

Improved Optimal and Approximate Power Graph Compression for Clearer Visualisation of Dense Graphs

https://arxiv.org/pdf/1311.6996v1.pdf

> you probably want more specialised tools like BLAS/LAPACK

The GraphBLAS and LAGraph are sparse matrix optimized libraries for this exact purpose:

https://github.com/DrTimothyAldenDavis/GraphBLAS

https://github.com/GraphBLAS/LAGraph/

I work on GraphBLAS, primarily on its LAGraph library and on tutorials. In the last few years, the GraphBLAS community has made a lot of progress on more efficient sparse matrix algorithms and porting graph algorithms to linear algebra – I hope LAGraph can play the role of a more efficient NetworkX in the future. The output of most LAGraph algorithms is a bunch of vectors/matrices so piping these into machine learning algorithms should be possible (and probably more efficient than using other representations).